How to Use the SmartChatAI MCP in CrewAI

Q: How do I set up specialized roles within SmartChatAI and CrewAI?

You use createnewaibot to establish each role (e.g., Researcher, Analyst). Each bot needs a name and an initial prompt that defines its specialty before the crew can work together.

Q: If I need my agents to access old information from SmartChatAI and CrewAI, what should I use?

Always check getbotchathistory. This tool retrieves all past conversations. Giving the crew this history means they don't have to start every task from scratch.

Q: What if one of my agents needs to know about a specific bot’s settings in SmartChatAI and CrewAI?

You can use getchatbotdetails to pull the current configuration for that agent. This ensures all team members are working with accurate, up-to-date instructions.

Q: Can I track every bot I've created using SmartChatAI and CrewAI?

Yes, run listaichatbots. This gives you a master list of every single AI chatbot instance running on your account. It’s the first step to managing your entire team.

Run complex, multi-role tasks with CrewAI using the SmartChatAI MCP Server for specialized agent teams.

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Connect SmartChatAI MCP to CrewAI

Create your Vinkius account to connect SmartChatAI to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup SmartChatAI with CrewAI

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Coordinating Agent Actions

CrewAI runs multiple agents. To get them to interact, you'll use `message_ai_chatbot` as a primary action point. This allows one specialized agent (the monitor) to pass information directly to another (the responder). The MCP Server handles the messaging layer.

Building Specialized Knowledge Bases

Your crew needs shared memory. You can train it using `add_text_to_knowledge_base` for quick definitions, or use `add_pdf_to_knowledge_base` if the source material is a large report. The knowledge base supports multiple inputs.

Discovering and Indexing Web Content

If your crew needs to research something, start by running `scrape_domain_links`. This tool discovers and indexes links across an entire domain. You can then pass those discovered links into a knowledge base for the agents to read.

Setup guide

Set up SmartChatAI MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke SmartChatAI tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="SmartChatAI Analyst",
    goal="Access and analyze SmartChatAI data via MCP.",
    backstory="Expert analyst with direct SmartChatAI access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent SmartChatAI transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="SmartChatAI Analyst",
        goal="Access and analyze SmartChatAI data via MCP.",
        backstory="Expert analyst with direct SmartChatAI access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent SmartChatAI transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by SmartChatAI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about SmartChatAI MCP in CrewAI

You use `create_new_ai_bot` to establish each role (e.g., Researcher, Analyst). Each bot needs a name and an initial prompt that defines its specialty before the crew can work together.

Always check `get_bot_chat_history`. This tool retrieves all past conversations. Giving the crew this history means they don't have to start every task from scratch.

You can use `get_chatbot_details` to pull the current configuration for that agent. This ensures all team members are working with accurate, up-to-date instructions.

Yes, run `list_ai_chatbots`. This gives you a master list of every single AI chatbot instance running on your account. It’s the first step to managing your entire team.

The server manages user profiles and chat history, which are handled by `get_authenticated_user_profile` and storing conversation transcripts.

Use it with your favorite AI tools

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